论文标题
动态的贝叶斯在自我决策的方法
Dynamic Bayesian Approach for decision-making in Ego-Things
论文作者
论文摘要
本文提出了一种基于多感官数据和特征选择的动态系统异常的新方法来检测异常。提出的方法通过考虑观察到的数据的多个特征来产生多个推理模型。这项工作有助于获得最精确的特征，以预测未来的实例和检测异常。生长的神经气（GNG）用于将多感官数据聚类到一组节点中，这些节点提供了对数据的语义解释，并为预测目的定义了局部线性模型。我们的方法使用马尔可夫跳跃粒子滤波器（MJPF）进行状态估计和异常检测。所提出的方法可用于选择要在网络操作中共享的最佳集合功能，例如国家预测，决策和异常检测过程受到青睐。通过使用由移动车辆组成的实际数据集评估这项工作，该数据集在受控环境中执行某些任务。
This paper presents a novel approach to detect abnormalities in dynamic systems based on multisensory data and feature selection. The proposed method produces multiple inference models by considering several features of the observed data. This work facilitates the obtainment of the most precise features for predicting future instances and detecting abnormalities. Growing neural gas (GNG) is employed for clustering multisensory data into a set of nodes that provide a semantic interpretation of data and define local linear models for prediction purposes. Our method uses a Markov Jump particle filter (MJPF) for state estimation and abnormality detection. The proposed method can be used for selecting the optimal set features to be shared in networking operations such that state prediction, decision-making, and abnormality detection processes are favored. This work is evaluated by using a real dataset consisting of a moving vehicle performing some tasks in a controlled environment.
